chore: file not needed

Former-commit-id: 01a923196312ced88a2f0ca2010e793c26c84855

dalle_mini/vqgan_jax/convert_pt_model_to_jax.py

@@ -1,109 +0,0 @@
-import re
-
-import jax.numpy as jnp
-from flax.traverse_util import flatten_dict, unflatten_dict
-
-import torch
-
-from modeling_flax_vqgan import VQModel
-from configuration_vqgan import VQGANConfig
-
-
-regex = r"\w+[.]\d+"
-
-
-def rename_key(key):
-    pats = re.findall(regex, key)
-    for pat in pats:
-        key = key.replace(pat, "_".join(pat.split(".")))
-    return key
-
-
-# Adapted from https://github.com/huggingface/transformers/blob/ff5cdc086be1e0c3e2bbad8e3469b34cffb55a85/src/transformers/modeling_flax_pytorch_utils.py#L61
-def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
-    # convert pytorch tensor to numpy
-    pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
-
-    random_flax_state_dict = flatten_dict(flax_model.params)
-    flax_state_dict = {}
-
-    remove_base_model_prefix = (flax_model.base_model_prefix not in flax_model.params) and (
-        flax_model.base_model_prefix in set([k.split(".")[0] for k in pt_state_dict.keys()])
-    )
-    add_base_model_prefix = (flax_model.base_model_prefix in flax_model.params) and (
-        flax_model.base_model_prefix not in set([k.split(".")[0] for k in pt_state_dict.keys()])
-    )
-
-    # Need to change some parameters name to match Flax names so that we don't have to fork any layer
-    for pt_key, pt_tensor in pt_state_dict.items():
-        pt_tuple_key = tuple(pt_key.split("."))
-
-        has_base_model_prefix = pt_tuple_key[0] == flax_model.base_model_prefix
-        require_base_model_prefix = (flax_model.base_model_prefix,) + pt_tuple_key in random_flax_state_dict
-
-        if remove_base_model_prefix and has_base_model_prefix:
-            pt_tuple_key = pt_tuple_key[1:]
-        elif add_base_model_prefix and require_base_model_prefix:
-            pt_tuple_key = (flax_model.base_model_prefix,) + pt_tuple_key
-
-        # Correctly rename weight parameters
-        if (
-            "norm" in pt_key
-            and (pt_tuple_key[-1] == "bias")
-            and (pt_tuple_key[:-1] + ("bias",) in random_flax_state_dict)
-        ):
-            pt_tensor = pt_tensor[None, None, None, :]
-        elif (
-            "norm" in pt_key
-            and (pt_tuple_key[-1] == "bias")
-            and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
-        ):
-            pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
-            pt_tensor = pt_tensor[None, None, None, :]
-        elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
-            pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
-            pt_tensor = pt_tensor[None, None, None, :]
-        if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
-            pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
-        elif pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and pt_tuple_key not in random_flax_state_dict:
-            # conv layer
-            pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
-            pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
-        elif pt_tuple_key[-1] == "weight" and pt_tuple_key not in random_flax_state_dict:
-            # linear layer
-            pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
-            pt_tensor = pt_tensor.T
-        elif pt_tuple_key[-1] == "gamma":
-            pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
-        elif pt_tuple_key[-1] == "beta":
-            pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
-
-        if pt_tuple_key in random_flax_state_dict:
-            if pt_tensor.shape != random_flax_state_dict[pt_tuple_key].shape:
-                raise ValueError(
-                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
-                    f"{random_flax_state_dict[pt_tuple_key].shape}, but is {pt_tensor.shape}."
-                )
-
-        # also add unexpected weight so that warning is thrown
-        flax_state_dict[pt_tuple_key] = jnp.asarray(pt_tensor)
-
-    return unflatten_dict(flax_state_dict)
-
-
-def convert_model(config_path, pt_state_dict_path, save_path):
-    config = VQGANConfig.from_pretrained(config_path)
-    model = VQModel(config)
-
-    state_dict = torch.load(pt_state_dict_path, map_location="cpu")["state_dict"]
-    keys = list(state_dict.keys())
-    for key in keys:
-        if key.startswith("loss"):
-            state_dict.pop(key)
-            continue
-        renamed_key = rename_key(key)
-        state_dict[renamed_key] = state_dict.pop(key)
-
-    state = convert_pytorch_state_dict_to_flax(state_dict, model)
-    model.params = unflatten_dict(state)
-    model.save_pretrained(save_path)